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Abstract

The histopathological examination of tissue specimens is essential for cancer
diagnosis and grading. However, this examination is subject to a considerable
amount of observer variability as it mainly relies on visual interpretation of
pathologists. To alleviate this problem, it is very important to develop computational
quantitative tools, for which image segmentation constitutes the core step.
The segmentation algorithms in literature commonly use pixel-level color/texture
descriptors that they define on image pixels for quantizing a tissue. On the other
hand, it is usually harder to express domain specific knowledge about tissues,
such as the spatial organization of tissue components, using only the pixel-level
descriptors. This may become even harder for tissue images, which typically consist
of a considerable amount of variation and noise at their pixel-level, such as
similar color distribution of different tissue components, distortion in cell alignments,
and color contrast caused by too much stain in a particular region. The
previous segmentation algorithms are more susceptible to these problems as they
work on pixel-level descriptors.
In order to successfully address these issues, in this thesis, we introduce three
new texture descriptors, namely ObjSEG, GraphRLM, and ObjCooc textures,
and implement algorithms that use these descriptors for segmenting histopathological
tissue images. We extract these texture descriptors on tissue components
that are approximately represented by circular objects. Since these objectoriented
texture descriptors are defined on the tissue components, and hence
domain specific knowledge, they represent the spatial organization of the components
better than their previous counterparts. Thus, our algorithms based on
these descriptors give more effective and robust segmentation results. Furthermore,
since the descriptors are not directly defined on image pixels, they are
effective to alleviate the pixel-level problems.
In our experiments, we tested our algorithms that use the proposed objectoriented
descriptors on a dataset of 200 colon tissue images. Our experiments
demonstrated that our new object-oriented feature descriptors led to high segmentation
accuracies, also providing a reasonable number of segmented regions.
Compared with its previous counterparts, the experimental results also showed
that our proposed algorithms are more effective in segmenting histopathological
images.